Method and apparatus for estimating a range of a moving object

ABSTRACT

A method for estimating a range of a moving object (MO) includes steps of capturing (S1) images of a surrounding by a camera (2), processing (S2) features of captured images to determine a bearing of a moving object (MO) based on a detected cluster of features belonging to the moving object (MO) within the captured images, and estimating (S3) a range of the moving object (MO) based on determined ground features belonging to a ground plane (GP) along the determined bearing of the moving object (MO) which are not occluded by the moving object (MO).

FIELD OF THE INVENTION

The invention relates to a method and apparatus for estimating a rangeof a moving object, in particular for estimating a range of a movingobject such as a pedestrian in the vicinity of a vehicle.

BACKGROUND INFORMATION

Vehicles are increasingly equipped with driver assistance systems whichassist the driver of the vehicle in performing driving maneuvers. Tosupport the driver vehicles can comprise vehicle cameras which aremounted to the vehicle's chassis on different sides. These camerascapture images of the vehicle's surrounding. From the captured images, asurround view of the vehicle's surrounding can be calculated anddisplayed to the driver of the vehicle. A vehicle can comprise a frontview camera, a rear view camera and two side view cameras to captureimages of the surrounding. The driver assistance system of the vehiclecan use the processed camera images to provide assistance functions tothe driver during driving maneuvers. For instance, the driver assistancesystem can support the driver in performing a parking maneuver. Aconventional driver assistance system can also provide securityfunctions on the basis of the processed camera images. More and moredriving maneuvers are performed semi-automatically or even fullyautomatically using driver assistance functions.

For many use cases, it is important to detect a range of a moving objectin the vicinity of a vehicle. This moving object can comprise forinstance a pedestrian or another moving object such as a car or avehicle moving in the vehicle's surrounding. For instance, during aparking maneuver, it is essential that the vehicle's chassis does notcollide with a pedestrian moving in the vehicle's surrounding.

SUMMARY OF THE INVENTION

Accordingly, it is an object of the present invention to provide amethod and apparatus for estimating a range of a moving object.

According to a first aspect, the invention provides a method forestimating a range of a moving object comprising the steps:

-   -   capturing images of a surrounding by at least one camera,    -   processing features of captured images to determine a bearing of        a moving object on the basis of, a detected cluster of features        belonging to the moving object within the captured images, and    -   estimating a range of the moving object on the basis of ground        features belonging to a ground plane along the determined        bearing of the moving object which are not occluded by the        moving object.

In a possible embodiment of the method according to the first aspect ofthe present invention, from the determined ground features not occludedby the moving object the ground feature having a maximum distance isselected and the range of the moving object is estimated according tothe maximum distance.

In a possible embodiment of the method according to the first aspect ofthe present invention, images of a vehicle's surrounding are captured bya vehicle camera of a vehicle while the vehicle is moving.

In a still further possible embodiment of the method according to thefirst aspect of the present invention, features in the captured imagesare detected and matched in each captured image to generate featuretracks between positions of corresponding features in the capturedimages.

In a further possible embodiment of the method according to the firstaspect of the present invention, position coordinates of the featuresare converted into normalized homogeneous image coordinates using cameracalibration.

In a further possible embodiment of the method according to the firstaspect of the present invention, a translation and rotation of thecamera during a time period between captured images is determined on thebasis of the vehicle's speed, V, the vehicle's steering angle, α, and onthe basis of a wheelbase, W.

In a further possible embodiment of the method according to the firstaspect of the present invention, a three-dimensional point cloud offeatures indicating a position of each feature in a three-dimensionalspace is calculated on the basis of the normalized homogeneous imagecoordinates and the calculated translation and rotation of the camera.

In a further possible embodiment of the method according to the firstaspect of the present invention, an essential matrix is calculated for amotion of the camera on the basis of the determined rotation andtranslation of the camera.

In a further possible embodiment of the method according to the firstaspect of the present invention, the epipolar constraint is applied tothe tracked features using the essential matrix.

In a further possible embodiment of the method according to the firstaspect of the present invention, the tracked features are segmented intofeatures belonging to moving objects and into features belonging tostatic objects on the basis of an error function, which measures howwell the features fulfill the epipolar constraint, wherein the errorfunction comprises the algebraic distance, geometric distance,reprojection error or sampson error.

In a further possible embodiment of the method according to the firstaspect of the present invention, the tracked features are segmented intofeatures belonging to moving objects and into features belonging tostatic objects on the basis of other suitable methods, such as measuringthe variance of a error function over time.

In a further possible embodiment of the method according to the firstaspect of the present invention, the segmented features belonging tomoving objects are clustered and a convex hull around the segmentedfeatures of each cluster is calculated.

In a further possible embodiment of the method according to the firstaspect of the present invention, an azimuth bearing of a moving objectis determined on the basis of the calculated convex hull around acluster of segmented features belonging to the moving object.

In a further possible embodiment of the method according to the firstaspect of the present invention, ground features are determined byselecting triangulated features belonging to static objects with aheight below a threshold height.

According to a second aspect, the invention provides an apparatusadapted to estimate a range of a moving object wherein said apparatuscomprises a processing unit adapted to process features of capturedimages to determine a bearing of a moving object on the basis of adetected cluster of features belonging to the moving object within thecaptured images, and adapted to estimate a range of the moving object onthe basis of ground features belonging to a ground plane along thedetermined bearing of the moving object which are not occluded by themoving object.

According to a third aspect, the invention provides a vehicle comprisingat least one vehicle camera adapted to capture images of the vehicle'ssurrounding and comprising an apparatus according to the second aspectof the present invention adapted to estimate a range of a moving objectin the vehicle's surrounding.

In a possible embodiment of the vehicle according to the third aspect ofthe present invention, the moving object comprises a pedestrian oranother vehicle in the vehicle's surrounding.

BRIEF DESCRIPTION OF THE DRAWINGS

In the following, possible embodiments of the different aspects of thepresent invention are described in more detail with reference to theenclosed figures.

FIG. 1 shows a schematic block diagram for illustrating a possibleexemplary embodiment of a vehicle comprising an apparatus for estimationof a range of a moving object according to an aspect of the presentinvention;

FIG. 2 shows a flowchart of a possible exemplary embodiment of a methodfor estimating a range of a moving object according to an aspect of thepresent invention;

FIG. 3 shows a schematic diagram for illustrating the operation of amethod and apparatus according to the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS OF THE INVENTION

As can be seen in the schematic block diagram of FIG. 1, a vehicle 1comprises in the illustrated embodiment an apparatus 3 adapted toestimate a range of a moving object MO in the vehicle's surrounding. Theapparatus 3 can form part of a driver assistance system of the vehicle1. The vehicle 1 comprises in the illustrated exemplary embodimentdifferent vehicle cameras 2-1, 2-2, 2-3, 2-4. The vehicle 1 can drivewith a velocity or speed V in a specific direction as illustrated inFIG. 1. In the example illustrated in FIG. 1, a moving object MO iswithin the field of view FoV of vehicle camera 2-3 mounted on the leftside of the vehicle's chassis.

The moving object MO shown schematically in FIG. 1 can be for instance apedestrian or another vehicle moving in the vicinity of the vehicle 1.The apparatus 3 receives from the different vehicle cameras 2-i digitalcamera images. The apparatus 3 comprises a processing unit adapted toprocess features of captured images to determine a bearing B of themoving object MO on the basis of a detected cluster of featuresbelonging to the moving object MO within the captured images of therespective vehicle camera, i.e. a single vehicle camera 2-3 in theillustrated example of FIG. 1. The processing unit is further adapted toestimate a range of the moving object MO on the basis of ground featuresbelonging to a ground plane in an area or bearing range along thedetermined bearing B of the moving object MO which are not occluded bythe moving object MO. The range of the moving object MO is the distanceof the moving object MO to the vehicle's chassis in the illustratedexample of FIG. 1. The number of vehicle cameras 2-i can differ indifferent vehicles. The vehicle 1 shown in FIG. 1 can be for instance acar or a truck moving on a road or moving on a parking space.

FIG. 2 shows a flowchart of a possible exemplary embodiment of a methodfor estimating a range of a moving object MO according to an aspect ofthe present invention. In the illustrated embodiment, the methodcomprises three main steps S1, S2, S3.

In a first step S1, images of a surrounding are captured by a camera.

In a further step S2, the features of captured images are processed todetermine a bearing B of a moving object MO on the basis of a detectedcluster of features belonging to the moving object MO within thecaptured images of the camera.

Finally, in step S3, a range of the moving object MO is estimated on thebasis of determined ground features belonging to a ground plane in abearing range around the determined bearing B of the moving object MOwhich are not occluded by the moving object MO.

In a possible embodiment, images are captured by vehicle cameras of avehicle 1. The images of the vehicle's surrounding are captured by atleast one vehicle camera 2 of a vehicle 1 while the vehicle 1 is moving.The camera images are taken at different times. The camera imagesinclude a ground plane around the vehicle and may include at least onemoving object MO such as a pedestrian or another vehicle. In a possibleembodiment, the camera images are captured at a predetermined frame ratewhich can comprise for instance 1 to 50 frames per second. The framerate can depend on the used hardware platform, the CPU load, inparticular how many other algorithms are running on the same processingunit. The camera 2 captures a sequence of camera images wherein the gapbetween two camera images can vary between 1/50th of a second and onesecond for the above-mentioned frame rates. At a velocity or speed of upto around 60 km/h, this means that many of the features captured in afirst camera image are still visible in the second (next) camera image.This is important because two views or camera images of the groundfeatures or ground plane around the vehicle 1 are necessary so that thethree-dimensional feature positions can be calculated when usingStructure From Motion, SFM, to triangulate static features. Withincreasing speed, V, of the vehicle 1, the frame rates of the framescaptured by the vehicle camera can be increased. Depending on thevelocity, V, of the vehicle 1, features visible in a first image arestill visible in the second image of the camera image sequence so thatground plane features can be reconstructed when using Structure FromMotion, SFM, to produce a three-dimensional point cloud of detectedfeatures.

Features in the captured images can be detected and matched in eachcaptured image to generate feature tracks between positions ofcorresponding features in the captured images. Features can be detectedand matched in each camera image to produce a list of features x1, andx2, wherein x1 is a list of positions of features in a first image andx2 is a list of positions of the same features in the second (next)camera image. Features that cannot be matched are discarded. Thefeatures can be calculated and detected by a feature detector such asHarris corner detector, FAST or SIFT. The two lists of the features inthe first camera image and of the features in the second camera imageform feature tracks or feature correspondences.

In a possible embodiment, coordinates of the features can be convertedinto normalized homogeneous image coordinates using camera calibration.For a pin-hole camera model with a focal length f, this can be expressedas follows:

$\begin{pmatrix}{y\; 1} \\{y\; 2} \\1\end{pmatrix} \sim {\begin{pmatrix}1 & 0 & 0 & 0 \\0 & 1 & 0 & 0 \\0 & 0 & {1\text{/}f} & 0\end{pmatrix}\begin{pmatrix}{p\; 1} \\{p\; 2} \\{p\; 3} \\1\end{pmatrix}}$wherein

$\quad\begin{pmatrix}{p\; 1} \\{p\; 2} \\{p\; 3} \\1\end{pmatrix}$are the three-dimensional coordinates of a point P in athree-dimensional space,

-   f is the focal length of the camera, which is known from camera    calibration,    and wherein

$\quad\begin{pmatrix}{y\; 1} \\{y\; 2} \\1\end{pmatrix}$are the normalized homogeneous coordinates, i.e. a projection of point Ponto the image plane.

For non-pin-hole camera models (such as cameras with fisheye lenses),similar equations can be used.

Position coordinates of the features can be converted into normalizedhomogeneous image coordinates using camera calibration. Intrinsiccalibration describes the properties of the camera lens so that pixelsmap to rays of light from the camera. Extrinsic calibration describesthe position and orientation of the camera mounted on the vehicle'schassis. This means that image pixel coordinates can be converted intonormalized homogeneous image coordinates. Using camera calibration tomap a pixel coordinate to a ray of light in three-dimensional space is awell-known technique in the field of computer vision.

In a possible embodiment, a translation and rotation of the vehiclecamera 2 during a time period between captured images is determined onthe basis of the vehicle's speed, V, the vehicle's steering angle, α,and on the basis of a wheelbase, W.

The change in the position of the camera 2 between a first image and asubsequent second image can be calculated in a possible embodiment byintegrating the speed and steering data of the vehicle 1 over a timeperiod between the frame of the first image and the frame of the secondimage.

Given a wheelbase, W, a speed, V, and a steering angle, α, the change inrotation Δφ of the vehicle 1 over a time period Δt, i.e. the period oftime between capturing the first and second images, is as follows:

${\Delta\;\varphi} = {\frac{V\;\Delta\; t}{W}\tan\;\alpha}$

The resulting motion matrix can be expressed as follows:

$\left\lbrack R_{car} \middle| t_{car} \right\rbrack = \begin{bmatrix}{\cos\;\Delta\;\varphi} & {\sin\;\Delta\;\varphi} & 0 & {{{- W}\;\cos\;\Delta\;\varphi} - {V\;\Delta\; t} + W} \\{{- \sin}\;\Delta\;\varphi} & {\cos\;\Delta\;\varphi} & 0 & {V\;\sin\;\Delta\;\varphi} \\0 & 0 & 1 & 0 \\0 & 0 & 0 & 1\end{bmatrix}$

Using the known position of the vehicle camera 2 on the vehicle 1, thiscan be converted into a translation and rotation of the camera[R_(camera)/t_(camera)].

In alternative embodiments, the change in position of the camera 2 canbe also determined by using visual egomotion or using GPS. Differentmethods for determining the change in position can also be fusedtogether. In a possible implementation, a Kalman filter can be used tocombine GPS data with visual egomotion of the vehicle 1 to determine achange in position.

In a possible embodiment, a three-dimensional point cloud of featuresindicating a position of each feature in a three-dimensional space canbe calculated on the basis of the normalized homogeneous imagecoordinates and the calculated translation and rotation of the camera 2.Structure From Motion, SFM, can be performed to generate athree-dimensional point cloud. The position of each feature in thethree-dimensional space can be calculated using the normalizedhomogeneous coordinates and the knowledge of the camera translation androtation. The three-dimensional point cloud can be produced from asequence of images. This point cloud can be a composite ofthree-dimensional points produced from several hundred image frames. Itis possible to generate a three-dimensional point cloud on the basis ofat least two subsequent image frames. In a possible embodiment, thethree-dimensional point clouds can be stored for a predetermined periodof time. If three-dimensional points in the cloud become obscured by anobject this information can provide further clues as to where a movingobject MO is located. In other possible embodiments, also radar or Lidarcan be used to calculate a three-dimensional point cloud of featuresindicating a position of features in a three-dimensional space aroundthe vehicle 1.

In a possible embodiment, an essential matrix E can be calculated for amotion of the camera 2 on the basis of the determined rotation andtranslation of the respective camera.

$E = {{{R_{camera}\left\lbrack t_{camera} \right\rbrack}_{x}\left\lbrack t_{cam} \right\rbrack}_{x} = \begin{bmatrix}0 & {- t_{3}} & t_{2} \\t_{3} & 0 & {- t_{1}} \\{- t_{2}} & t_{1} & 0\end{bmatrix}}$

The essential matrix E can then be used in a cost function to measurehow well the features meet the epipolar constraint.

This cost function can then be used to segment the features. Suitablecost functions comprise a measurement of the alegbraic distance,geometric distance, reprojection error or sampson error.

In a possible embodiment, the tracked features can be segmented intofeatures belonging to a moving object MO and into features belonging toa static object SO on the basis of the calculated error. For instance bycomparing the calculated error with a threshold value ε_(threshold). Thevalue of the threshold value ε_(threshold) can depend on the accuracy ofthe optical flow and can depend on the accuracy and latency of theestimates of the camera position.

In a possible embodiment, the segmented features belonging to movingobjects MO can be clustered and a convex hull around the segmentedfeatures of each cluster can be calculated. In a possibleimplementation, the segmented features can be clustered by applying adensity-based spatial clustering of applications with noise (DBSCAN)algorithm to the segmented features. The convex hull is then calculatedaround the segmented features of the moving object MO.

FIG. 3 shows an example of a cluster with a convex hull H around amoving object MO. In the illustrated example, the moving object MO is apedestrian moving on a parking lot with different parking spaces on theground. In the illustrated example, a baby carriage BC is alsopositioned on the ground comprising static feature points. In theillustrated example of FIG. 3, a plurality of detected featuresbelonging to a ground plane GP are illustrated. The ground features canbe determined by selecting triangulated features belonging to staticobjects with a height below a threshold height. The value of thethreshold height can be adjusted. The baby carriage BC illustrated inFIG. 3 also forms a static object SO having structural components whichcan be used to detect features such as intersections of the structure ofthe baby carriage BC. Further, the lines on the ground plane GP can alsobe used for detecting features f (e.g. f₁, f₂, f₃, f₄) of the groundplane GP. Some features in the illustrated example are segmented asbelonging to a moving object MO. These features are segmented to form acluster and a convex hull H is calculated around the segmented featuresof the respective cluster. The bearing B (see FIG. 1) of the movingobject MO, e.g. the pedestrian illustrated in FIG. 3, is determined onthe basis of the detected cluster of features belonging to the movingobject MO. In a possible embodiment, an azimuth bearing i.e. azimuthangle of a moving object MO can be determined on the basis of thecalculated convex hull H around a cluster of segmented featuresbelonging to the respective moving object MO. In a possible embodiment,a range of values of azimuth bearings i.e. angles can be determined bytaking the minimum and maximum bearings of the points of featuresincluded in the convex hull H. Alternatively, a minimum or maximum ofazimuth bearings B1, B2 of the convex hull H itself can be used (seeFIG. 1 and FIG. 3). In a further possible embodiment, the size of themoving object MO can also be taken into account. For example, theminimum or maximum of the lower half of the respective moving object canbe used if the object is not substantially rectangular.

FIG. 3 shows a determined azimuth bearing value range of the movingobject MO between bearings B1, B2 with respect to the camera 2 of thevehicle 1. As can be seen in FIG. 3, there are several features f₁, f₂,f₃, f₄ of the ground plane GP within the estimated bearing range (i.e.angular range of bearings) between bearing B1 and bearing B2 in front ofthe moving object MO (i.e. between the moving object MO and the camera)which are not occluded by the moving object MO, i.e. pedestrian. In theexample of FIG. 3, there are four features f₁, f₂, f₃, f₄ belonging tothe ground plane GP within the determined bearing range of the movingobject MO, which are not occluded by the moving object MO. The range,i.e. distance, of the moving object MO, i.e. pedestrian, can beestimated on the basis of the determined ground features belonging tothe ground plane GP along the determined bearing B of the moving objectMO which are not occluded by the moving object MO. The bearing B can becalculated as an average value of the minimum bearing B1 and the maximumbearing B2. In a possible embodiment, from among the determined groundfeatures, the ground feature having a maximum distance is selected. Inthe illustrated example of FIG. 3, feature f₄ located within the bearingrange between B1, B2 and comprising a maximum distance to the vehiclecamera 2 is selected and the range of the moving object MO is derivedfrom this maximum distance. Ground points, i.e. ground plane features,can be determined by searching for triangulated features with roughlyzero height. Alternatively, a ground plane GP can be fitted to thetriangulated points using for instance a RANSAC algorithm or leastsquares approximation to find features belonging to the ground plane GP.The range of the moving object MO can then be estimated by consideringall triangulated static three-dimensional ground points within theazimuth bearing range of the moving object MO and selecting those groundplane features comprising the maximum distance from the vehicle camera2.

Cameras with fisheye lenses can produce a dense three-dimensional pointcloud for ground plane features close to the vehicle 1. Therefore, theestimate of the range of the moving object MO gets more accurate as themoving object MO gets closer to the vehicle 1. The calculated estimateis always closer than the real distance to the moving object MO,reducing therefore the chances of overestimating a distance and thedanger of colliding with the moving object MO. In the simple example ofFIG. 3, the distance to the moving object MO is estimated on the basisof the distance between the camera 2 and feature f₄ belonging to theground plane GP as the ground plane feature within the bearing rangebeing farthest away from the vehicle camera 2 but not occluded by themoving object MO.

The method according to the present invention makes use of the fact thattwo-dimensional features can be tracked throughout the camera image anda three-dimensional ground plane can be reconstructed as the vehicle 1moves. A moving object MO moving across the ground plane GP occludesthree-dimensional points on the ground plane. This occlusion can be usedto estimate a range i.e. distance of the moving object MO. The methodcan be used to estimate a range i.e. distance of different kinds ofmoving objects MO such as other vehicles or pedestrians. It is notrequired that the moving object MO is a rigid object. It is also notnecessary that many features can be tracked on the object. With themethod according to the present invention, a range i.e. distance of anymoving object MO moving on a ground plane in a vehicle's surrounding canbe estimated. The method can be used in any situation where there is aneed to track non-rigid objects that are moving on a ground plane usinga single camera 2 mounted on a moving vehicle 1. The method can be usedfor detection of pedestrians, for a backup warning or for automaticbraking. A backup warning system can be implemented in which athree-dimensional model is created of static objects SO and additionallymoving objects MO are tracked and clustered in the two-dimensionalimage, wherein a range i.e. distance can be estimated from occludedpoints on the three-dimensional ground plane. A warning can beautomatically produced and output to a driver of the vehicle 1 when amoving object MO is detected that could result in a collision with thevehicle 1.

The invention claimed is:
 1. A method of estimating a distance to amoving object, comprising the steps: capturing images of a surroundingenvironment by a camera; by processing the images, detecting therein acluster of features belonging to the moving object, and determining anazimuth angle of the moving object based on the cluster of features; inthe images, determining static ground features that belong to a groundplane in the surrounding environment, and that are located along theazimuth angle of the moving object between the camera and the movingobject, and that are not occluded from the images by the moving object;selecting a most-distant one of the static ground features that has amaximum distance from the camera among all of the determined staticground features; and estimating a distance to the moving object based onthe maximum distance of the most-distant static ground feature.
 2. Themethod according to claim 1, wherein the surrounding environment is asurrounding environment of a vehicle, the camera is a vehicle camera ofthe vehicle, and the capturing of the images is performed by the vehiclecamera while the vehicle is moving.
 3. The method according to claim 1,further comprising detecting and matching corresponding ones of therespective features among successive ones of the images to generatefeature tracks between respective positions of corresponding matchedfeatures in the images.
 4. The method according to claim 1, furthercomprising determining and converting position coordinates of therespective features into normalized homogeneous image coordinates usingcamera calibration.
 5. The method according to claim 2, furthercomprising determining a translation and a rotation of the vehiclecamera during a time period between successive ones of the images basedon a speed, a steering angle, and a wheelbase of the vehicle.
 6. Themethod according to claim 5, further comprising determining andconverting position coordinates of the respective features intonormalized homogeneous image coordinates using camera calibration, andcalculating a 3D point cloud of the features indicating a respectiveposition of each respective one of the features in a three-dimensionalspace based on the normalized homogeneous image coordinates and thetranslation and the rotation of the vehicle camera.
 7. The methodaccording to claim 6, further comprising calculating an essential matrixfor a motion of the vehicle camera based on the rotation and thetranslation of the vehicle camera.
 8. The method according to claim 1,further comprising calculating errors related to an epipolar constraintof the features, and segmenting the features into moving featuresbelonging to moving objects and static features belonging to staticobjects based on the errors.
 9. The method according to claim 8, furthercomprising clustering the moving features into feature clusters, andcalculating a convex hull respectively around the moving features ofeach one of the feature clusters.
 10. The method according to claim 9,wherein the determining of the azimuth angle of the moving object isbased on the convex hull calculated around the feature cluster belongingto the moving object.
 11. The method according to claim 1, wherein thedetermining of the ground features comprises selecting triangulatedfeatures belonging to static objects with a height below a thresholdheight.
 12. The method according to claim 1, wherein the azimuth angleis a range of azimuth angle values spanned by the moving object from aminimum azimuth angle value to a maximum azimuth angle value.
 13. Themethod according to claim 1, wherein the azimuth angle is an averagevalue of a minimum azimuth angle value and a maximum azimuth angle valuebounding an azimuth angle range spanned by the moving object.
 14. Anapparatus for estimating a distance to a moving object, comprising: acamera configured to capture images of a surrounding environment; and aprocessing unit configured: to process the images, so as to detecttherein a cluster of features belonging to the moving object, and so asto determine an azimuth angle of the moving object based on the clusterof features; to determine, in the images, static ground features thatbelong to a ground plane in the surrounding environment, and that arelocated along the azimuth angle of the moving object between the cameraand the moving object, and that are not occluded from the images by themoving object; to select a most-distant one of the static groundfeatures that has a maximum distance from the camera among all of thedetermined static ground features; and to estimate a distance to themoving object based on the maximum distance of the most-distant staticground feature.
 15. A vehicle comprising a vehicle body and theapparatus according to claim
 14. 16. The vehicle according to claim 15,wherein the moving object is a pedestrian or another vehicle in thesurrounding environment.